Training latent dynamics models for long-horizon stability requires explicitly optimizing for multi-step rollout accuracy, not just reconstruction—this restructures the solution space in ways that conventional metrics don't capture.
This paper shows that neural surrogate models for physics simulations fail during long predictions not because of poor compression, but because they're trained only to reconstruct data. The authors introduce training techniques—including Koopman operator learning and noise injection—that restructure the latent space to support stable long-horizon forecasting.